Selection of weighing functions in H<inf>&#x221E;</inf> controller design using PBIL

Abstract

H<sub>&#x221E;</sub> optimal control technique is seen as a promising robust control technique that can effectively deal with the problems of model uncertainties. However, for H<sup>&#x221E;</sup> optimal control design to be successful one must be able to choose adequate performance and uncertainty weights. Until now, there is no a systematic way of choosing these weighting functions; they are generally selected based on trial and error. This approach not only is ineffective but also time consuming. In this paper, a systematic way of selecting the weighting functions in H<sub>&#x221E;</sub> optimal control is proposed. The selection of adequate weighting function is formulated as an optimization problem and solved using Population Based Incremental Learning (PBIL) Algorithm.

DOI: 10.1109/IJCNN.2014.6889930

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Cite this paper

@article{Munawa2014SelectionOW, title={Selection of weighing functions in H controller design using PBIL}, author={P. Munawa and Komla A. Folly}, journal={2014 International Joint Conference on Neural Networks (IJCNN)}, year={2014}, pages={1733-1738} }